Presentation attacks are one of the many dangers facing law enforcement today. In addition, material science is constantly advancing and criminals, aware of this fact, are taking advantage of new composites to manufacture new artifacts that allow them to cross borders by breaching border control points. This article presents the creation of several presentation attacks using make-up, hyper-realistic latex, and prosthetic masks. It is worth noting that such attacks do not receive adequate attention, due to the difficulty in their elaboration. The work of professionals in the make-up sector is required. Each stage of processing is analyzed for any artifacts that would facilitate the detection of the attack, using a multispectral approach in the visible and thermal spectra. The methodology evaluates three different face recognition systems (FRS), the different stages of impersonation, i.e. when a specific part of the face such as nose, cheekbones, jaw, or eyes is incorporated. The results show that certain parts of the face improve impersonation and make it more difficult for the algorithms to detect possible impersonation. However, other parts of the face, such as the jaw, not only do not improve impersonation but also significantly worsen performance. Using OpenFace as an FRS example, which is one of the FRS employed in this research work, the bonafide comparison of the target yields a score of 0.304, while with the make-up attack before applying make-up to the jaw, it gives 0.291, and after applying make-up, it gives 0.421.

This study introduces a reinforcement training framework for face recognition systems (FRSs) that leverages facial morphing techniques to generate counterfactual visual instances for model enhancement. Two complementary morphing strategies were employed: a geometric approach based on Delaunay–Voronoi triangulation (DVT‐Morph) and a generative approach using latent diffusion and autoencoder‐based models (diffusion‐based morphing [MorDIFF]). The generated morphs act as controlled counterfactuals, representing minimally modified facial images that induce changes in FRS verification decisions. The proposed method integrates these counterfactuals into the training process of two state‐of‐the‐art recognition systems, ArcFace and MagFace, to strengthen their decision boundaries and improve their robustness, calibration, and explainability. By combining morphing‐based counterfactual generation with eXplainable Artificial Intelligence (XAI) techniques, the framework enables a more interpretable embedding space and increased resilience against morphing and adversarial perturbations. The experimental results demonstrate that the inclusion of morph‐based counterfactuals significantly enhances the verification accuracy and decision transparency of modern FRSs. Moreover, the methodology is model‐ and morphing‐agnostic and can be applied to any FRS architecture, regardless of the morphing generation technique.

As the demand for expansive back-end systems in online applications continues to grow, novel frameworks are necessitated to address the escalating operational demands, energy consumption, and associated costs. Traditional Client–Server models, while offering centralized security and reliability, are characterized by their high deployment and maintenance expenses. Conversely, Peer-to-Peer (P2P) models, despite being cost-effective and scalable, are hindered by inherent security and data integrity challenges. Moreover, the lack of a central authority in P2P systems complicates a definitive resolution of scenarios involving stakes, where users cannot withdraw without incurring a tangible loss. In this research work, a hybrid back-end framework is introduced, combining the advantages of both models through the utilization of cryptographic algorithms and Secure Multi-Party Computation (MPC) protocols. The baseline solution is lightweight and fully composable, making it capable of utilizing different more complex slot-in MPC techniques. The proposed framework’s effectiveness is demonstrated through a simplified two-player Spades game, although it is fully generalizable to any application. Evaluations across multiple case studies reveal substantial performance enhancements compared to conventional approaches, particularly post-initialization, highlighting the scheme’s potential as a cost-effective, energy-efficient, and secure solution for modern online applications.